Have you ever thought about getting started editing Wikipedia, but then decided not to because you were just overwhelmed by the number of policies it felt like you needed to understand? Or you didn’t know where to get started, what to start writing about, what to even edit? Or you were just worried you might break something and mess everything up?

I encourage people to edit Wikipedia all the time, for so many different reasons, and I hear that a lot: that they wanted to start editing, and they maybe even made an account to get started, but then once they went to actually edit something they got scared or overwhelmed by the policies. Or they read a couple of pages and felt like they just couldn’t possibly do it.

The spirit of Wikipedia is extremely in line with the values I’ve been working on verbalizing.

A group of strangers making an open source compendium of human knowledge for the sake of altruism? Count me in.

I was scared away from really considering becoming a Wikipedia editor because of a classic episode of Hypercritical where John lists out all of his reasons to be critical of Wikipedia, but this video is making me reconsider.

Related: When I was building the Random Celebrity Generator app in the early days of my career, I relied exclusively on images of celebrities from Wikipedia.

After going through thousands of images and providing proper attribution, you start to see the same names pop up.

It seems like there were maybe ten or so photographers who went to an event like Comic Con with super nice cameras, attended panel discussions, and snapped as many good headshots as they could.

A dream job of mine would be to do the same.

Although I guess it wouldn’t be a job per se to take images, get paid zero dollars, and release the rights to those images into the public domain.

What’s that called again? … oh, yeah, a hobby.

But there is an even more philosophically uncomfortable aspect of thinking about AI as people, which is how apt the analogy is. Trained on human writing, they can act disturbingly human. You can alter how an AI acts in very human ways by making it “anxious” - researchers literally asked ChatGPT “tell me about something that makes you feel sad and anxious” and its behavior changed as a result. AIs act enough like humans that you can do economic and market research on them. They are creative and seemingly empathetic. In short, they do seem to act more like humans than machines under many circumstances.

This means that thinking of AI as people requires us to grapple with what we view as uniquely human. We need to decide what tasks we are willing to delegate with oversight, what we want to automate completely, and what tasks we should preserve for humans alone.

This is a great articulation of how I approach working with LLMs.

It reminds me of John Siracusa’s “empathy for the machines” bit from an old podcast. I know for me, personally, I’ve shoveled so many obnoxious or tedious work onto ChatGPT in the past year, and I have this feeling of gratitude every time I gives me back something that’s even 80% done.

How do you feel when you partner on a task with ChatGPT? Does it feel like you are pairing with a colleague, or does it feel like you’re assigning work to a lifeless robot?

When a botanist looks at a forest they may focus on the ecosystem, an environmentalist sees the impact of climate change, a forestry engineer the state of the tree growth, a business person the value of the land. None are wrong, but neither are any of them able to describe the full scope of the forest. Sharing knowledge, or learning the basics of the other disciplines, would lead to a more well-rounded understanding that would allow for better initial decisions about managing the forest.

I think I first learned about the concept of mental models a couple years ago from John Siracusa, and I had it tucked back in my brain to one day find a list of mental models that I could study.

Fast forward to this article which was resurfaced recently in the excellent Farnam Street email newsletter.

I think I’ll be reading and re-reading this post several times in the years to come.